Diabetic Retinopathy Detection by means of Deep Learning

IEEE BASE PAPER ABSTRACT:

Diabetic Retinopathy (DR) is a complication caused by diabetes that affects the human eye. It is caused by the mutilation of the blood vessels of the light-sensitive tissue at the back of the human retina. It’s the most recurrent cause of blindness in the working-age group of people and is highly likely when diabetes is poorly controlled. Although, methods to detect Diabetic Retinopathy exist, they involve manual examination of the retinal image by an Ophthalmologist. The Proposed approach of DR detection aims to detect the complication in an automated manner using Deep Learning. The model is trained using a GPU on 35126 retinal images released publicly by eyePACS on the Kaggle website and achieved an accuracy of approximately 81%.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

InceptionV3 Architecture

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Sumit Thorat, Akshay Chavan, Pratik Sawant, Sharvika Kulkarni, Nitin Sisodiya, Prof. Anand Kolapkar, “Diabetic Retinopathy Detection by means of Deep Learning”, IEEE Conference, 2021.

Python IEEE Projects

Diabetic Retinopathy Detection by means of Deep Learning